Combining Diagnostics and Artificial Intelligence enabling Precision Medicine, Risk Prediction and Prognosis to Guide Therapy.

LIVERFASt GP+ (LIVERSTAT), first-line screening tool in at-risk MAFLD patients outperformed standard of-care (SOC) FIB-4

Poster presented for EASL June 2023

The primary aim was to construct and validate a new MAFLD screening test, GP+, noninferior to the SOC reference, FIB-4, for advanced fibrosis (F3F4 stages), and with less drawbacks related to age cutoffs.

LIVERFASt GP+(LIVERSTAT), a non-invasive blood testing for NAFLD staging improves risk stratification of patients with indeterminate FIB-4 results

Poster presented for EASL June 2023

The primary aim was to assess retrospectively the GP+ (LIVERSTAT) performance and concordance rate (CR) with liver biopsy and Fibroscan as second step after FIB-4 for the assessment of advanced fibrosis (F3F4) in NAFLD patients.

Comparative assessment of noninvasive methods (nims) – LiverFast, liver stiffness measurement (lsm) with transient elastography (te, fibroscan), elf and fib-4 – in a prospective cohort with chronic liver diseases (cld) from a tertiary liver center

Poster Presented June 2022

In a prospective tertiary cohort with CLD, to assess clinical performance against liver biopsy of different NIMs :

For advanced and bridging fibrosis: LIVERFASt Fibrosis test, Enhanced liver fibrosis score (ELF), FIB-4 and LSM using vibration controlled transient elastography (VCTE).

For steatosis (mild, moderate and marked): LIVERFASt Steatosis test and CAP (Fibroscan) in NAFLD patients, including a control group with CLD without steatosis (S0)].

Repeated noninvasive liver biopsy surrogate LiverFast correlates with bmi and liver enzymes improvements

Poster Presented June 2022

To assess liver fibrosis regression rate (LFR) USING repeated LIVERFASt AND CORRELATIONS with IMPROVEMENTS IN clinical endpoints, body mass index
BMI ≥ 10% and liver enzymes
ALT ≥ 50%from baseline.

Simulating clinical confidence intervals for black-box algorithmic predictions of liver steatosis.

Poster Presented September 2020

Clinicians have begun using blood-serum biomarkers with artificial intelligence algorithms (AIAs) to assess the degree of liver steatosis without taking a liver biopsy. However, intra-patient and intra-lab variability could affect the inputs, and with >5 biomarkers used by an AIA, noise is compounded. Interpretable measures of an AIA’s confidence are absent in the clinical workflow. We aim to resolve this gap in interpretability of non-invasive AIA with a stochastic noise injection method and interactive data visualization—allowing clinicians to a) observe steatosis predictions under simulated noise conditions and b) interactively simulate expected regression of steatosis with respect to changes in biomarkers through course of treatment.

Comparative assessment liver lesions using non-invasive serum biomarkers LiverFast, fib4, apri and liver stiffness measurement (lsm, fibroscan) in chronic hepatitis c (chc) patients with liver biopsy.

Poster Presented September 2020

Despite the high efficacy of current direct acting agents (DAA), in Thailand CHC is still a leading cause of liver-related morbidity and mortality and staging of liver fibrosis is critical for the management of outcomes in patients even after viral cure. LIVERFAStTM (LF, Fibronostics, US) is a proprietary technology based in serum biomarkers to assess quantitatively liver fibrosis (LF-Fib), necroinflammatory (NAI) activity (LF-Act) and steatosis (LF-Ste).

Predictive value of non-invasive methods LiverFast, acoustic radiation force impulse (arfi), fib-4 and apri to identify the natural phases of chronic hepatitis b (chb) infection from the national university hospital (nuh) chb study cohort of Singapore.

Poster Presented September 2020

In order to determine the outcomes and progression to significant liver fibrosis (SLF) as per ARFI, we set up a prospective NUH HBV cohort with chronic HBV infection (Ch.Inf) expected to have no/minimal liver disease vs moderate/severe in chronic hepatitis (Ch.Hep) patients (pts).(JHepatol2017) LIVERFASt (LF, Fibronostics, US), is a patented technology to assess liver fibrosis(LF-F) and activity(LF-A).

Evaluating serum biomarkers LiverFast surrogates of liver fibrosis and steatosis could identify risks in a clinical population experiencing sars-cov2 infection (covid19).

Poster Presented September 2020

Coronavirus disease-2019 (COVID-19) is a life-threatening infection caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus. Age, diabetes and metabolic factors has rapidly emerged as a major comorbidity for COVID-19 severity. However, the phenotypic characteristics of patients (pts) in COVID-19 are unknown. For clinicians, it’s imperative to predict the outcome of a given patient following a positive test for SARS-CoV2—it is known that prior health history and demographics are informative towards describing the wide range of prognostic outcomes for COVID19 pts.

Comparative performances of liverfast, vtce (fibroscan) and other serum non-invasive tests (nits) for the diagnosis of advanced chronic liver disease in non-alcoholic fatty liver disease (nafld) patients from a cohort with liver biopsy.

Poster Presented September 2020

There is a call for action in the management of patients (pts) with type 2 diabetes mellitus (T2DM) and steatohepatitis (NASH) LIVERFASt (LF) is a serum-based proprietary panel for assessing fibrosis (LF-Fib), steatosis (LF-Ste) and activity (LF-Act) in NAFLD pts.

Monitoring fatty liver disease during pre/post-bariatric surgery with non-invasive LiverFast

Poster Presented June 2020

According to the American Society for Metabolic and Bariatric Surgery (ASMBS)1, non-alcoholic fatty liver disease (NAFLD) is one of the obesity-related co-morbidities that qualifies a patient to undergo a bariatric surgery if BMI> 35. By 2030, it is predicted that nearly half of adults in the USA will have obesity2. Over 80% of the patients with obesity submitted to bariatric surgery suffer from (NAFLD), with 25% – 55% resulting in steatohepatitis (NASH) and 2% – 12% liver fibrosis and cirrhosis3.

Introducing liverfast in your clinic: simplifying liver assessment in medicine

Poster Presented May 2020

Dr. Sam Pappas uses the capabilities of Artificial Intelligence (AI) algorithm LIVERFASt blood-based test for evaluation of liver disease, fatty liver disease and NASH (Non-Alcoholic Steatohepatitis) to provide excellent care for his patients.

Machine learning technology for evaluation of liver fibrosis, inflammation activity and steatosis (liverfast)

Poster Presented April 2020

Use of non-invasive liver tests in extended populations is evaluated in 13068 patients who underwent the LIVERFASt test for fatty liver disease assessment. Data evaluation revealed 11% of the patients exhibited significant fibrosis, approximately 7% of the population had severe hepatic inflammation, and steatosis was observed in most patients, 63%, whereas severe steatosis S3 was observed in 20%. Using modified SAF (Steatosis, Activity and Fibrosis) scores obtained using the LIVERFASt algorithm, NAFLD was detected in 13.41% of the patients.

Assessment of fatty liver disease using a biomarker-based non-invasive algorithm liverfast test in south-east asia

Poster Presented January 2020

Scientific poster presented at the NASH-TAG conference, January 2020 for “Assessment of fatty liver disease using a biomarker-based non-invasive algorithm LIVERFASt test in South-East Asia”.

At NASH-TAG international conference, clinicians and researchers share the latest advances and challenges in the diagnosis and therapy of NASH and liver fibrosis.

Non-invasive assessment of liver fibrosis, inflammation and steatosis

Poster Presented November 2019

The LIVERFASt machine learning-based algorithm uses a combination of anthropometric and serum biomarkers that are individually used to provide fatty liver disease staging. It is a reliable, and reproducible tool which provides grading or staging of the three liver lesions: fibrosis, inflammation activity and steatosis.

Non-invasive testing for fatty liver disease for primary care providers

Poster Presented November 2018

High sensitivity and specificity indicating “Elevated” or “Low” risk for NAFLD can be achieved using a simple algorithm based on minimal information from patients seeking routine check-ups from Primary Care Physicians or PCPs. The algorithmic result is significantly more precise than current reliance on identification of outlier values for standard individual liver function biomarkers. Furthermore, most patients at elevated risk can be evaluated for quantitative SAF score prediction using additional biomarkers – without undergoing expensive or invasive procedures of elastography, imaging or biopsy.